Install the package to reproduce the report here:
library(readr) #need this package to import the datasetThe SARS 2003 Outbreak Complete Dataset which was collected for my assignment purpose by Kaggle.com was downloaded from https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset/download. The dataset includes 2538 observations and 5 variables and the details of these variables are given below:
Date reported: Date
Country effected: Characters
Cumulative number of case(s): Integer
Number of death: Integer
Number of recovered cases: Integer
Downloaded the data from the above url.
The data is imported from the working directory and saved as Sars_2003 into R studio with the help of read_csv Function.
head() function is used to display initial observations of the data set along with the header.
#step 1: Importing the dataset Sars_2003.csv into Rstudio
Sars_2003 <- read_csv("Sars_2003.csv")## Parsed with column specification:
## cols(
## Date = col_date(format = ""),
## Country = col_character(),
## `Cumulative number of case(s)` = col_double(),
## `Number of deaths` = col_double(),
## `Number recovered` = col_double()
## )#step 2: view the initial observation with headers
head(Sars_2003)#step 3: checking dimensions of data drame sars_2003
dim(Sars_2003)## [1] 2538 5#step 4: checking class of each variables:
class(Sars_2003$Date)## [1] "Date"class(Sars_2003$Country)## [1] "character"class(Sars_2003$`Cumulative number of case(s)`)## [1] "numeric"class(Sars_2003$`Number of deaths`)## [1] "numeric"class(Sars_2003$`Number recovered`)## [1] "numeric"#step 5: Checking the column names
colnames(Sars_2003)## [1] "Date" "Country"
## [3] "Cumulative number of case(s)" "Number of deaths"
## [5] "Number recovered"#step 6: Checking structure of data frame:
str(Sars_2003)## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 2538 obs. of 5 variables:
## $ Date : Date, format: "2003-03-17" "2003-03-17" ...
## $ Country : chr "Germany" "Canada" "Singapore" "Hong Kong SAR, China" ...
## $ Cumulative number of case(s): num 1 8 20 95 2 1 40 2 8 0 ...
## $ Number of deaths : num 0 2 0 1 0 0 1 0 2 0 ...
## $ Number recovered : num 0 0 0 0 0 0 0 0 0 0 ...
## - attr(*, "spec")=
## .. cols(
## .. Date = col_date(format = ""),
## .. Country = col_character(),
## .. `Cumulative number of case(s)` = col_double(),
## .. `Number of deaths` = col_double(),
## .. `Number recovered` = col_double()
## .. )#step 7: Displaying the data:
head(Sars_2003)Displaying the initial ten observations of data frame Sars_2003 using indexing[selecting first ten rows and all columns] and saving it in the variable named Sars_2003_a
Converting the subset of data frame into a matrix using as.matrix() function.
The new matrix is saved as variable(Sars_matrix).
Checking the Class and Structure of the new matrix(sars_matrix) using str() an class function.
As per the observsation, the matrix obatained is a character matrix. This is so because matrix can have values of only one datatype. Therefore, all the elements in the matrix have been converted to character.
#step 8: Subsetting data frame to produce intial 10 observation
Sars_2003_a <- Sars_2003[1:10,]
Sars_2003_a#step 9: convert the data frame to matrix
sars_matrix <- as.matrix(Sars_2003_a)
#step 10: check structure and class of new matrix
str(sars_matrix)## chr [1:10, 1:5] "2003-03-17" "2003-03-17" "2003-03-17" "2003-03-17" ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:5] "Date" "Country" "Cumulative number of case(s)" "Number of deaths" ...Subsetting data frame to comprise only the first and the last variable.Therefore, it shows all the observations and choosing the first and last column only.
Saving the sars_FL as an R object file(.RData file) using save() function
#step 11: subsetting data frame to comprise first and last variable only
sars_FL <- Sars_2003[,c(1,5)]
head(sars_FL)#step 12: saving the data frame as R object file.
save(sars_FL, file="save_Fl.RData")Creating a fresh data frame(Sudent_info) using data. frame() function.The data frame contains two variables and 10 observations: Student_ID(numeric) which highlights the Student Unique Id. Grade(ordinal) which shows the grade of each individual in a particular semester(HD,D,P)
The level of factor variable(grade)is ordered
A new variable was created and added to the data frame(Student_info)
Checking the structure, class, attribute and dimension of new data frame using class,str,dim,attributes functions.
# step 13: crating new data frame with 2 variables and 10 observations:
Student_info <- data.frame(Student_ID= c(001,002,003,004,005,006,007,008,009,010),
Grade= c("HD","HD","P","D","D,","HD","P","P","D","HD"))
Student_info#Step 14: Factorising dataframe
Student_info$Grade <- factor(Student_info$Grade, levels = c("HD","D","P"),ordered = TRUE)
Student_info$Grade## [1] HD HD P D <NA> HD P P D HD
## Levels: HD < D < P#Step 15: Creating numeric variable
sem <- c(1L,4L,3L,2L,1L,4L,3L,6L,7L,5L)
#Step 13 : Adding the new variable to data frame
Student_info2 <- cbind(Student_info, sem)
#Step 16: Check structure, class, attribute and dimension of new data frame
class(Student_info2)## [1] "data.frame"str(Student_info2)## 'data.frame': 10 obs. of 3 variables:
## $ Student_ID: num 1 2 3 4 5 6 7 8 9 10
## $ Grade : Ord.factor w/ 3 levels "HD"<"D"<"P": 1 1 3 2 NA 1 3 3 2 1
## $ sem : int 1 4 3 2 1 4 3 6 7 5dim(Student_info2)## [1] 10 3attributes(Student_info2)## $names
## [1] "Student_ID" "Grade" "sem"
##
## $class
## [1] "data.frame"
##
## $row.names
## [1] 1 2 3 4 5 6 7 8 9 10